VARH-AI at SemEval-2026 Task 10: Exploiting Architectural Diversity with Transformer-SSM Ensembles and Confidence-Based Iterative Refinement for Conspiracy Detection

AbstractThis paper describes our system for SemEval 2026 Task 10 (PsyCoMark), focusing on Subtask 2: binary conspiracy classification in Reddit submission statements. We present a heterogeneous ensemble approach that combines Transformer-based models (DeBERTa, RoBERTa) with State-Space Models (Mamba) to leverage architectural diversity for improved generalization. Our key contributions include: (1) Bidirectional Mamba (BiMamba), adapting state-space sequence models for bidirectional document classification; (2) (2) a safety-switched multi-task training setup that uses marker supervision only for gold-annotated samples, preventing noisy pseudo-labeled rows from affecting the span extraction objective; and (3) Confidence-Based Iterative Refinement, using committee voting for high-quality pseudo-label generation. Our best official submission achieved a weighted F1 score of 0.78 on the Subtask 2 test set, ranking 4th on the public CodaBench leaderboard. We provide detailed ablation studies demonstrating the complementary contributions of each architectural component to inform future research directions.